Dilated MultiResUNet: Dilated multiresidual blocks network based on U-Net for biomedical image segmentation

U-net is a classical and high-efficiency network, which achieves better performance than other end-to-end networks in Biomedical Image Segmentation with fewer training images. However, the feature details of images are often missing out of pooling layers and the contour of small objects cannot be totally reconstructed by the upsampling layers of U-Net. To reduce the loss of important features, we propose a Dilated MultiResUNet network to improve the performance of end-to-end image segmentation based on U-Net, Res2Net, MultiResUNet, Dilated Residual Networks and Squeeze-and-Excitation Networks. We use Improved Multi Block, Res Block, and Dilated Multi Block to substitute for common convolutional operation. Besides, we design two special networks of Dilated MultiResUNet with various combination of blocks to improve up-sampling and concatenation operation of U-Net. We evaluate the proposed models in terms of typical evaluation indexes on four biomedical datasets, i.e. ISBI2015, INBreast, blood vessel and psoriasis dataset. The experimental results show that the two Dilated MultiResUNet networks have superior accuracy and achieve better generalization performance only with 59.7 % and 63.3 % number of parameters of U-Net model respectively.

Automated summary

This study proposed a Dilated MultiResUNet network to improve end-to-end image segmentation performance based on U-Net, Res2Net, MultiResUNet, Dilated Residual Networks, and Squeeze-and-Excitation Networks. Evaluation on four biomedical datasets showed superior accuracy and generalization performance with significantly fewer parameters compared to the U-Net model.